class_docstr: A csv data provider.
docstr: Returns keras input as explained in data.py module.
docstr_tok: ["Returns","keras","input","as","explained","in","data.py","module","."]
code: def get_keras_input(self, batch_size):
      """Returns keras input as explained in data.py module."""
      train_dataset = (
          tf.keras.preprocessing.image_dataset_from_directory(
              directory=self._input_dir,
              labels="inferred",
              label_mode="binary",
              class_names=None,
              color_mode="rgb",
              batch_size=batch_size,
              image_size=(self._image_height, self._image_width),
              shuffle=True,
              seed=73,
              validation_split=self._eval_fraction,
              subset="training",
              interpolation="bilinear",
              follow_links=False,
          )
      )
  
      train_dataset = train_dataset.cache().prefetch(
          buffer_size=tf.data.experimental.AUTOTUNE
      )
  
      validation = tf.keras.preprocessing.image_dataset_from_directory(
          directory=self._input_dir,
          labels="inferred",
          label_mode="binary",
          class_names=None,
          color_mode="rgb",
          batch_size=batch_size,
          image_size=(self._image_height, self._image_width),
          shuffle=True,
          seed=73,
          validation_split=self._eval_fraction,
          subset="validation",
          interpolation="bilinear",
          follow_links=False,
      )
  
      return train_dataset, None, validation
  
file_path: model_search/model_search/data/image_data_for_binary.py
imports: from absl import flags
  from model_search.data import data
  import tensorflow.compat.v2
label_desc: Use image node_id 6 for calling the Provider obj's underlying member method code with example usage: obj.get_keras_input(batch_size) and returns: train_dataset,  None,  validation